onnx--onnx/docs/docsgen/source/expect_onnxruntime.md

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(l-function-expect)=

# Sample operator test code

Many examples from the documentation end by calling
function `expect` to check a runtime returns the expected
outputs for the given example. Here is one implementation
based on [onnxruntime](https://onnxruntime.ai/).

```
from typing import Any, Sequence
import numpy as np
import onnx
import onnxruntime


def expect(
    node: onnx.NodeProto,
    inputs: Sequence[np.ndarray],
    outputs: Sequence[np.ndarray],
    name: str,
    **kwargs: Any,
) -> None:
    # Builds the model
    present_inputs = [x for x in node.input if (x != "")]
    present_outputs = [x for x in node.output if (x != "")]
    input_type_protos = [None] * len(inputs)
    if "input_type_protos" in kwargs:
        input_type_protos = kwargs["input_type_protos"]
        del kwargs["input_type_protos"]
    output_type_protos = [None] * len(outputs)
    if "output_type_protos" in kwargs:
        output_type_protos = kwargs["output_type_protos"]
        del kwargs["output_type_protos"]
    inputs_vi = [
        _extract_value_info(arr, arr_name, input_type)
        for arr, arr_name, input_type in zip(inputs, present_inputs, input_type_protos)
    ]
    outputs_vi = [
        _extract_value_info(arr, arr_name, output_type)
        for arr, arr_name, output_type in zip(
            outputs, present_outputs, output_type_protos
        )
    ]
    graph = onnx.helper.make_graph(
        nodes=[node], name=name, inputs=inputs_vi, outputs=outputs_vi
    )
    kwargs["producer_name"] = "backend-test"

    if "opset_imports" not in kwargs:
        # To make sure the model will be produced with the same opset_version after opset changes
        # By default, it uses since_version as opset_version for produced models
        produce_opset_version = onnx.defs.get_schema(
            node.op_type, domain=node.domain
        ).since_version
        kwargs["opset_imports"] = [
            onnx.helper.make_operatorsetid(node.domain, produce_opset_version)
        ]

    model = onnx.helper.make_model_gen_version(graph, **kwargs)

    # Checking the produces are the expected ones.
    sess = onnxruntime.InferenceSession(model.SerializeToString(),
                                        providers=["CPUExecutionProvider"])
    feeds = {name: value for name, value in zip(node.input, inputs)}
    results = sess.run(None, feeds)
    for expected, output in zip(outputs, results):
        assert_allclose(expected, output)
```